GARCH model

One of the most important methods employed to measure the market risk is value at risk calculation method. In this study, the value at risk of banks listed on the Tehran Stock Exchange and Over-the-counter (OTC) are calculated using parametric model, Monte Carlo simulation, historical simulation and Two-Sided Power (TSP) Distribution. The sample includes all listed banks in Iran. The results showed that the value at risk estimated by TSP and historical models is more accurate than the VaR estimated by Monte Carlo and GARCH models. TSP model and then historical model are more accurate than the other ones. Moreover, GARCH is the least accurate model. So far, no research has been conducted to investigate all four models of value at risk assessment. JEL Classification: E5, E58, J21

This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of them has some limitations. Therefore, these methods are not recommended in the case of complications in financial modeling since they require considering a series of assumptions, such as symmetric distributions in return on assets. Because the stock exchange data in the present study are skewed, asymmetric distributions along with symmetric distributions have been used for estimating VaR in this study. In this paper, the performance of fifteen VaR models with a compound of three conditional volatility characteristics including GARCH, APARCH and GJR and five distributional assumptions (normal, Student’s t, skewed Student’s t and two different Lévy distributions, include normal-inverse Gaussian (NIG) and generalized hyperbolic (GHyp)) for return innovations are investigated in the chemical, base metals, automobile, and cement industries. To do so, daily data from of Tehran Stock Exchange are used from 2013 to 2020. The results show that the GJR model with NIG distribution is more accurate than other models. According to the industry index loss function, the highest and lowest risks are related to the automotive and cement industries.

Examining the importance and influence of financial market companies is one of the main issues in the field of financial management because sometimes the collapse of a stock exchange company can affect an entire financial market. One systematic way to analyze the significance and impacts of companies is to use complex networks based on Interaction Graphs (IGs). There are different methods for quantifying the edge weight in an IG. In this method, the graph vertices represent the stock exchange companies that are connected by weighted edges (corresponding to the extent to which they relate to each other). In this paper, using the GARCH model (1,1) and the Clayton copula, we obtained the lower tail dependence interaction network of the first 52 companies of the Tehran Stock Exchange in terms of average market value, between June 2017 and October 2020. Then, based on the minimum spanning tree of the interaction network, we divided the companies into different communities. Using this classification, it was observed that the companies of the first group (Food Industry) and the second group (Oil Refinery) have the greatest impact on other companies. We also calculated the central indexes of the minimum spanning tree for each company. According to the results, the companies of the third group (Steel) have the highest average in the central indicators.

Financial markets are currently experiencing sharp volatility. Studying how the returns and volatility in one market affect other markets has always been one issue that helps investors and policymakers to make optimal decisions. Given the importance of volatility spillovers in the Iranian financial market, this study aimed to investigate the mechanisms behind the volatility spillovers in the foreign exchange, gold, and stock markets to the oil market in Iran. This descriptive study was conducted using the daily and monthly data from the oil, foreign exchange, gold, and capital markets from 2010 to 2019 and to analyze the data, ARCH and GARCH models have been used. The results of this study showed that the abnormal volatility of the foreign exchange and gold in the previous day positively affects the abnormal volatility of the oil market today, this indicates that money flows in the currency market, spilling over the fluctuations into the oil market. hey also found that the abnormal volatility of the capital market in the previous day negative affects the abnormal volatility of the oil market today, indicating that if money flows in the capital market, which indicates the flow of money in the capital market from yesterday, increasing the transfer of emotions to the current capital market but does not spillover into the oil market and volatility is not transferred into the oil market. Overall, the findings of this study confirmed the positive impact of the foreign exchange and gold markets on the abnormal volatility in the oil market in the short term (daily) and long term (monthly), but did not confirm the positive impact of the capital market on the abnormal volatility in the oil market.